Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic
Department of Epidemiology, Cardiovascular Disease Prevention, and Health Promotion, National Institute of Cardiology, Warsaw, Poland.
Br J Sports Med. 2022 Dec;56(23):1366-1374. doi: 10.1136/bjsports-2021-105198. Epub 2022 Sep 7.
To determine the net effect of different physical activity intervention components on step counts in addition to self-monitoring.
A systematic review with meta-analysis and meta-regression.
Five databases (PubMed, Scopus, Web of Science, ProQuest and Discus) were searched from inception to May 2022. The database search was complemented with backward and forward citation searches and search of the references from relevant systematic reviews.
Randomised controlled trials comparing an intervention using self-monitoring (active control arm) with an intervention comprising the same treatment PLUS any additional component (intervention arm).
The effect measures were mean differences in daily step count. Meta-analyses were performed using random-effects models, and effect moderators were explored using univariate and multivariate meta-regression models.
Eighty-five studies with 12 057 participants were identified, with 75 studies included in the meta-analysis at postintervention and 24 at follow-up. At postintervention, the mean difference between the intervention and active control arms was 926 steps/day (95% CI 651 to 1201). At a follow-up, the mean difference was 413 steps/day (95% CI 210 to 615). Interventions with a prescribed goal and involving human counselling, particularly via phone/video calls, were associated with a greater mean difference in the daily step count than interventions with added print materials, websites, smartphone apps or incentives.
Physical activity interventions that combine self-monitoring with other components provide an additional modest yet sustained increase in step count compared with self-monitoring alone. Some forms of counselling, particularly remote phone/video counselling, outperformed other intervention components, such as websites and smartphone apps.
CRD42020199482.
除自我监测外,确定不同身体活动干预成分对步数的净效应。
系统评价与荟萃分析和荟萃回归。
从开始到 2022 年 5 月,在五个数据库(PubMed、Scopus、Web of Science、ProQuest 和 Discus)中进行了检索。数据库检索辅以回溯和前向引文检索以及对相关系统评价参考文献的检索。
比较使用自我监测(主动对照组)的干预与包含相同治疗加任何额外成分(干预组)的干预的随机对照试验。
效应量为每日步数的平均差异。使用随机效应模型进行荟萃分析,并使用单变量和多变量荟萃回归模型探索效应调节因素。
共确定了 85 项研究,涉及 12057 名参与者,其中 75 项研究在干预后和 24 项研究在随访时进行了荟萃分析。干预后,干预组与主动对照组之间的平均差异为 926 步/天(95%CI 651 至 1201)。随访时,平均差异为 413 步/天(95%CI 210 至 615)。与增加印刷材料、网站、智能手机应用程序或激励措施的干预相比,设定目标并涉及人类咨询(特别是通过电话/视频通话)的干预与每日步数的平均差异更大。
与单独自我监测相比,将自我监测与其他成分相结合的身体活动干预措施可提供额外的适度但持续的步数增加。一些咨询形式,特别是远程电话/视频咨询,优于其他干预成分,如网站和智能手机应用程序。
CRD42020199482。